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Ivan Matantsev
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Merge remote-tracking branch 'upstream/master' into Ivanidze/LockdownHAL
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‎Microsoft.ML.sln‎

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Original file line numberDiff line numberDiff line change
@@ -33,6 +33,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.TestFramework"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Predictor.Tests", "test\Microsoft.ML.Predictor.Tests\Microsoft.ML.Predictor.Tests.csproj", "{6B047E09-39C9-4583-96F3-685D84CA4117}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Functional.Tests", "test\Microsoft.ML.Functional.Tests\Microsoft.ML.Functional.Tests.csproj", "{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}"
37+
EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.ResultProcessor", "src\Microsoft.ML.ResultProcessor\Microsoft.ML.ResultProcessor.csproj", "{3769FCC3-9AFF-4C37-97E9-6854324681DF}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.FastTree", "src\Microsoft.ML.FastTree\Microsoft.ML.FastTree.csproj", "{B7B593C5-FB8C-4ADA-A638-5B53B47D087E}"
@@ -928,6 +930,18 @@ Global
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{5E920CAC-5A28-42FB-936E-49C472130953}.Release-Intrinsics|Any CPU.Build.0 = Release-Intrinsics|Any CPU
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{5E920CAC-5A28-42FB-936E-49C472130953}.Release-netfx|Any CPU.ActiveCfg = Release-netfx|Any CPU
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{5E920CAC-5A28-42FB-936E-49C472130953}.Release-netfx|Any CPU.Build.0 = Release-netfx|Any CPU
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{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Debug|Any CPU.Build.0 = Debug|Any CPU
935+
{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug-Intrinsics|Any CPU
936+
{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Debug-Intrinsics|Any CPU.Build.0 = Debug-Intrinsics|Any CPU
937+
{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Debug-netfx|Any CPU.ActiveCfg = Debug-netfx|Any CPU
938+
{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Debug-netfx|Any CPU.Build.0 = Debug-netfx|Any CPU
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{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Release|Any CPU.ActiveCfg = Release|Any CPU
940+
{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Release|Any CPU.Build.0 = Release|Any CPU
941+
{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Release-Intrinsics|Any CPU.ActiveCfg = Release-Intrinsics|Any CPU
942+
{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Release-Intrinsics|Any CPU.Build.0 = Release-Intrinsics|Any CPU
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{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Release-netfx|Any CPU.ActiveCfg = Release-netfx|Any CPU
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{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5}.Release-netfx|Any CPU.Build.0 = Release-netfx|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
@@ -1011,6 +1025,7 @@ Global
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{85D0CAFD-2FE8-496A-88C7-585D35B94243} = {09EADF06-BE25-4228-AB53-95AE3E15B530}
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{31D38B21-102B-41C0-9E0A-2FE0BF68D123} = {D3D38B03-B557-484D-8348-8BADEE4DF592}
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{5E920CAC-5A28-42FB-936E-49C472130953} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4}
1028+
{CFED9F0C-FF81-4C96-8D5E-0436264CA7B5} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4}
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EndGlobalSection
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GlobalSection(ExtensibilityGlobals) = postSolution
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SolutionGuid = {41165AF1-35BB-4832-A189-73060F82B01D}

‎build/Dependencies.props‎

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@@ -43,6 +43,8 @@
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<PropertyGroup>
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<BenchmarkDotNetVersion>0.11.3</BenchmarkDotNetVersion>
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<MicrosoftMLTestModelsPackageVersion>0.0.3-test</MicrosoftMLTestModelsPackageVersion>
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<MicrosoftMLTensorFlowTestModelsVersion>0.0.10-test</MicrosoftMLTensorFlowTestModelsVersion>
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<MicrosoftMLOnnxTestModelsVersion>0.0.4-test</MicrosoftMLOnnxTestModelsVersion>
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</PropertyGroup>
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</Project>

‎docs/code/MlNetCookBook.md‎

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@@ -688,11 +688,11 @@ var catColumns = data.GetColumn<string[]>(mlContext, "CategoricalFeatures").Take
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// Build several alternative featurization pipelines.
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var pipeline =
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// Convert each categorical feature into one-hot encoding independently.
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mlContext.Transforms.Categorical.OneHotEncoding("CategoricalFeatures", "CategoricalOneHot")
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mlContext.Transforms.Categorical.OneHotEncoding("CategoricalOneHot", "CategoricalFeatures")
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// Convert all categorical features into indices, and build a 'word bag' of these.
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.Append(mlContext.Transforms.Categorical.OneHotEncoding("CategoricalFeatures", "CategoricalBag", CategoricalTransform.OutputKind.Bag))
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.Append(mlContext.Transforms.Categorical.OneHotEncoding("CategoricalBag", "CategoricalFeatures", CategoricalTransform.OutputKind.Bag))
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// One-hot encode the workclass column, then drop all the categories that have fewer than 10 instances in the train set.
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.Append(mlContext.Transforms.Categorical.OneHotEncoding("Workclass", "WorkclassOneHot"))
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.Append(mlContext.Transforms.Categorical.OneHotEncoding("WorkclassOneHot", "Workclass"))
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.Append(mlContext.Transforms.FeatureSelection.CountFeatureSelectingEstimator("WorkclassOneHot", "WorkclassOneHotTrimmed", count: 10));
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// Let's train our pipeline, and then apply it to the same data.

‎docs/samples/Microsoft.ML.Samples/Dynamic/IidChangePointDetectorTransform.cs‎

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@@ -54,16 +54,9 @@ public static void IidChangePointDetectorTransform()
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// Setup IidSpikeDetector arguments
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string outputColumnName = nameof(ChangePointPrediction.Prediction);
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string inputColumnName = nameof(IidChangePointData.Value);
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var args = new IidChangePointDetector.Arguments()
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{
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Source = inputColumnName,
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Name = outputColumnName,
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Confidence = 95, // The confidence for spike detection in the range [0, 100]
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ChangeHistoryLength = Size / 4, // The length of the sliding window on p-values for computing the martingale score.
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};
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// The transformed data.
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var transformedData = new IidChangePointEstimator(ml, args).Fit(dataView).Transform(dataView);
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var transformedData = ml.Transforms.IidChangePointEstimator(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView).Transform(dataView);
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// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
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var predictionColumn = ml.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
@@ -119,16 +112,9 @@ public static void IidChangePointDetectorPrediction()
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// Setup IidSpikeDetector arguments
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string outputColumnName = nameof(ChangePointPrediction.Prediction);
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string inputColumnName = nameof(IidChangePointData.Value);
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var args = new IidChangePointDetector.Arguments()
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{
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Source = inputColumnName,
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Name = outputColumnName,
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Confidence = 95, // The confidence for spike detection in the range [0, 100]
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ChangeHistoryLength = Size / 4, // The length of the sliding window on p-values for computing the martingale score.
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};
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// Time Series model.
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ITransformer model = new IidChangePointEstimator(ml, args).Fit(dataView);
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ITransformer model = ml.Transforms.IidChangePointEstimator(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
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// Create a time series prediction engine from the model.
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var engine = model.CreateTimeSeriesPredictionFunction<IidChangePointData, ChangePointPrediction>(ml);

‎docs/samples/Microsoft.ML.Samples/Dynamic/IidSpikeDetectorTransform.cs‎

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@@ -51,16 +51,9 @@ public static void IidSpikeDetectorTransform()
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// Setup IidSpikeDetector arguments
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string outputColumnName = nameof(IidSpikePrediction.Prediction);
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string inputColumnName = nameof(IidSpikeData.Value);
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var args = new IidSpikeDetector.Arguments()
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{
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Source = inputColumnName,
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Name = outputColumnName,
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Confidence = 95, // The confidence for spike detection in the range [0, 100]
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PvalueHistoryLength = Size / 4 // The size of the sliding window for computing the p-value; shorter windows are more sensitive to spikes.
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};
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// The transformed data.
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var transformedData = new IidSpikeEstimator(ml, args).Fit(dataView).Transform(dataView);
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var transformedData = ml.Transforms.IidSpikeEstimator(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView).Transform(dataView);
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// Getting the data of the newly created column as an IEnumerable of IidSpikePrediction.
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var predictionColumn = ml.CreateEnumerable<IidSpikePrediction>(transformedData, reuseRowObject: false);
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// Setup IidSpikeDetector arguments
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string outputColumnName = nameof(IidSpikePrediction.Prediction);
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string inputColumnName = nameof(IidSpikeData.Value);
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var args = new IidSpikeDetector.Arguments()
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{
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Source = inputColumnName,
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Name = outputColumnName,
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Confidence = 95, // The confidence for spike detection in the range [0, 100]
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PvalueHistoryLength = Size / 4 // The size of the sliding window for computing the p-value; shorter windows are more sensitive to spikes.
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};
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// The transformed model.
120-
ITransformer model = new IidSpikeEstimator(ml, args).Fit(dataView);
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ITransformer model = ml.Transforms.IidChangePointEstimator(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
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// Create a time series prediction engine from the model.
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var engine = model.CreateTimeSeriesPredictionFunction<IidSpikeData, IidSpikePrediction>(ml);

‎docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs‎

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@@ -1,7 +1,8 @@
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using System;
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using System.Linq;
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using Microsoft.Data.DataView;
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using Microsoft.ML.Learners;
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using Microsoft.ML.Data;
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using Microsoft.ML.Trainers;
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using Microsoft.ML.SamplesUtils;
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using Microsoft.ML.Trainers.HalLearners;
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‎docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs‎

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@@ -1,6 +1,6 @@
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using System;
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using System.Linq;
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using Microsoft.ML.Learners;
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using Microsoft.ML.Trainers;
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namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
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{

‎docs/samples/Microsoft.ML.Samples/Dynamic/SsaChangePointDetectorTransform.cs‎

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// Setup SsaChangePointDetector arguments
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var inputColumnName = nameof(SsaChangePointData.Value);
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var outputColumnName = nameof(ChangePointPrediction.Prediction);
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var args = new SsaChangePointDetector.Arguments()
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{
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Source = inputColumnName,
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Name = outputColumnName,
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Confidence = 95, // The confidence for spike detection in the range [0, 100]
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ChangeHistoryLength = 8, // The length of the window for detecting a change in trend; shorter windows are more sensitive to spikes.
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TrainingWindowSize = TrainingSize, // The number of points from the beginning of the sequence used for training.
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SeasonalWindowSize = SeasonalitySize + 1 // An upper bound on the largest relevant seasonality in the input time series."
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};
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// The transformed data.
64-
var transformedData = new SsaChangePointEstimator(ml, args).Fit(dataView).Transform(dataView);
54+
var transformedData = ml.Transforms.SsaChangePointEstimator(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
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// Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
6757
var predictionColumn = ml.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
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// Setup SsaChangePointDetector arguments
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var inputColumnName = nameof(SsaChangePointData.Value);
122112
var outputColumnName = nameof(ChangePointPrediction.Prediction);
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var args = new SsaChangePointDetector.Arguments()
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{
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Source = inputColumnName,
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Name = outputColumnName,
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Confidence = 95, // The confidence for spike detection in the range [0, 100]
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ChangeHistoryLength = 8, // The length of the window for detecting a change in trend; shorter windows are more sensitive to spikes.
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TrainingWindowSize = TrainingSize, // The number of points from the beginning of the sequence used for training.
130-
SeasonalWindowSize = SeasonalitySize + 1 // An upper bound on the largest relevant seasonality in the input time series."
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};
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// Train the change point detector.
135-
ITransformer model = new SsaChangePointEstimator(ml, args).Fit(dataView);
115+
ITransformer model = ml.Transforms.SsaChangePointEstimator(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
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// Create a prediction engine from the model for feeding new data.
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var engine = model.CreateTimeSeriesPredictionFunction<SsaChangePointData, ChangePointPrediction>(ml);

‎docs/samples/Microsoft.ML.Samples/Dynamic/SsaSpikeDetectorTransform.cs‎

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@@ -56,18 +56,9 @@ public static void SsaSpikeDetectorTransform()
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// Setup IidSpikeDetector arguments
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var inputColumnName = nameof(SsaSpikeData.Value);
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var outputColumnName = nameof(SsaSpikePrediction.Prediction);
59-
var args = new SsaSpikeDetector.Arguments()
60-
{
61-
Source = inputColumnName,
62-
Name = outputColumnName,
63-
Confidence = 95, // The confidence for spike detection in the range [0, 100]
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PvalueHistoryLength = 8, // The size of the sliding window for computing the p-value; shorter windows are more sensitive to spikes.
65-
TrainingWindowSize = TrainingSize, // The number of points from the beginning of the sequence used for training.
66-
SeasonalWindowSize = SeasonalitySize + 1 // An upper bound on the largest relevant seasonality in the input time series."
67-
};
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6960
// The transformed data.
70-
var transformedData = new SsaSpikeEstimator(ml, args).Fit(dataView).Transform(dataView);
61+
var transformedData = ml.Transforms.SsaSpikeEstimator(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
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// Getting the data of the newly created column as an IEnumerable of SsaSpikePrediction.
7364
var predictionColumn = ml.CreateEnumerable<SsaSpikePrediction>(transformedData, reuseRowObject: false);
@@ -127,18 +118,9 @@ public static void SsaSpikeDetectorPrediction()
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// Setup IidSpikeDetector arguments
128119
var inputColumnName = nameof(SsaSpikeData.Value);
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var outputColumnName = nameof(SsaSpikePrediction.Prediction);
130-
var args = new SsaSpikeDetector.Arguments()
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{
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Source = inputColumnName,
133-
Name = outputColumnName,
134-
Confidence = 95, // The confidence for spike detection in the range [0, 100]
135-
PvalueHistoryLength = 8, // The size of the sliding window for computing the p-value; shorter windows are more sensitive to spikes.
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TrainingWindowSize = TrainingSize, // The number of points from the beginning of the sequence used for training.
137-
SeasonalWindowSize = SeasonalitySize + 1 // An upper bound on the largest relevant seasonality in the input time series."
138-
};
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140122
// Train the change point detector.
141-
ITransformer model = new SsaSpikeEstimator(ml, args).Fit(dataView);
123+
ITransformer model = ml.Transforms.SsaChangePointEstimator(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
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143125
// Create a prediction engine from the model for feeding new data.
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var engine = model.CreateTimeSeriesPredictionFunction<SsaSpikeData, SsaSpikePrediction>(ml);

‎docs/samples/Microsoft.ML.Samples/Static/SDCARegression.cs‎

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@@ -1,7 +1,7 @@
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using System;
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using Microsoft.ML.Data;
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using Microsoft.ML.Learners;
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using Microsoft.ML.StaticPipe;
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using Microsoft.ML.Trainers;
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66
namespace Microsoft.ML.Samples.Static
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{

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